Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Energy Line and Hydraulic Gradient Line

Energy Line and Hydraulic Gradient Line

Based on Bernoulli's equation, the energy line (EL) and hydraulic grade line (HGL) provide graphical representations of energy distribution in a fluid flow system. For steady, incompressible, inviscid flows, Bernoulli's equation is expressed as:

Maxwell-Boltzmann Distribution: Problem Solving

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by

Bernoulli's Equation: Problem Solving

Bernoulli's Equation: Problem Solving

A Venturi meter is essential for measuring fluid flow rates in pipelines. It utilizes the relationship between fluid velocity and pressure described by Bernoulli's equation. When installed in a sewage system, the Venturi meter accurately determines the wastewater flow rate by measuring pressure differences.
The first step is to compute the cross-sectional areas of the pipe and the Venturi throat to analyze the pressure difference indicated by the pressure gauge. Next, the continuity equation is...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

What is an Electrochemical Gradient?

What is an Electrochemical Gradient?

Adenosine triphosphate, or ATP, is considered the primary energy source in cells. However, energy can also be stored in the electrochemical gradient of an ion across the plasma membrane, which is determined by two factors: its chemical and electrical gradients.
The chemical gradient relies on differences in the abundance of a substance on the outside versus the inside of a cell and flows from areas of high to low ion concentration. In contrast, the electrical gradient revolves around an...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Biomarker screen-guided care for preterm birth risk in nulliparous pregnancies: a subgroup analysis of the PRIME randomized controlled trial.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians·2026

Same author

Scaling Up Bayesian Neural Networks with Neural Networks.

Transactions on machine learning research·2026

Same author

The Critical Period Microbiota Shape Brain Plasticity.

bioRxiv : the preprint server for biology·2026

Same author

A Bayesian Time-Varying Psychophysiological Interaction Model.

Data science in science·2026

Same author

Neurodatascience: Past, Present, and Future.

Data science in science·2026

Same author

A HORSESHOE MIXTURE MODEL FOR BAYESIAN SCREENING WITH AN APPLICATION TO LIGHT SHEET FLUORESCENCE MICROSCOPY IN BRAIN IMAGING.

The annals of applied statistics·2026

Search research articles

Related Experiment Videos

Neural network gradient Hamiltonian Monte Carlo.

Lingge Li¹, Andrew Holbrook², Babak Shahbaba¹

¹Donald Bren School of Information and Computer Sciences, University of California, Irvine, USA.

Computational Statistics

|November 8, 2019

Summary

This summary is machine-generated.

Hamiltonian Monte Carlo (HMC) sampling efficiency is improved by using neural networks to approximate gradients, reducing computational burden for large datasets while maintaining convergence to true distributions.

Keywords:

Bayesian inference MCMC Neural networks

Related Experiment Videos

Area of Science:

Computational Statistics
Bayesian Inference
Machine Learning

Background:

Hamiltonian Monte Carlo (HMC) is a key algorithm for Bayesian model posterior sampling.
HMC excels in high-dimensional spaces but faces computational challenges with large datasets due to repeated gradient calculations.

Purpose of the Study:

To develop a method for reducing the computational cost of HMC.
To investigate the use of neural networks for approximating gradients in HMC.

Main Methods:

A novel approach using neural networks to approximate gradient computations within the HMC algorithm.
Theoretical analysis to prove convergence properties of the proposed method.

Main Results:

The neural network approximation significantly reduces the computational burden of HMC.
Experimental validation on synthetic and real-world data confirms the method's efficacy.
The modified HMC maintains convergence to the true posterior distribution.

Conclusions:

Neural network-based gradient approximation offers a scalable solution for HMC.
This method enhances the practical applicability of HMC for large-scale Bayesian inference.